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AI based Robot Safe Learning and Control
Xuefeng Zhou,
Zhihao Xu,
Shuai Li 
,
Hongmin Wu,
Taobo Cheng,
Xiaojing Lv
,
Hongmin Wu,
Taobo Cheng,
Xiaojing Lv
Swansea University Author:
Shuai Li
-
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Distributed under the terms of a Creative Commons Attribution 4.0 (CC-BY) Licence. Copyright: The Editor(s) (if applicable) and The Author(s) 2020
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DOI (Published version): 10.1007/978-981-15-5503-9
Abstract
Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems,...
| ISBN: | 9789811555022 9789811555039 |
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| Published: |
Singapore
Springer Singapore
2020
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| Online Access: |
http://dx.doi.org/10.1007/978-981-15-5503-9 |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa55087 |
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| Abstract: |
Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities. |
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| Keywords: |
Safe Control, Deep Reinforcement Learning, Recurrent Neural Network, Force Contro, lObstacle Ovoidance, Adaptive Control, Trajectory Tracking, Open Access |
| College: |
Faculty of Science and Engineering |